About PaigeMiller

PaigeMiller · ‎02-11-2013

No, you'd have to combine the output data set from PROC MEANS or PROC SUMMARY with the original data. For example: data combined; merge originaldata procmeansoutput; by cust_id; run;

PaigeMiller · ‎02-11-2013

Proc Means or Proc Summary with a BY statement should get you the max value for each cust_id

PaigeMiller · ‎02-11-2013

Yes, you can do so, you would use the Fit Model command in the Analyze menu. Speaking as someone who has followed for decades the mathematical attempts to predict sports wins and losses, my advice to you is: don't bother, there must be something better you can do with your time.

PaigeMiller · ‎02-08-2013

Okay! So here's the problem. PROC FREQ doesn't do sums. Here is a multi-step program to get what you want. Maybe there is a shorter way, but this is how I would do it proc freq data=whatever; by somevariable; tables a/noprint out=var_a_freq; tables b/noprint out=var_b_freq; tables c/noprint out=var_c_freq; tables d/noprint out=var_d_freq; run; data all; set var_a_freq(where=(a='c')) var_b_freq(where=(b='c')) var_c_freq(where=(c='c')) var_d_freq(where=(d='c')); by somevariable; run; proc means data=all sum; by somevariable; var count; run;

PaigeMiller · ‎02-08-2013

I don't see how the "what you'd like to get" relates to the tables you are currently getting, nor do I see how the "what you'd like to get" relates to your original problem description. Is the "what you'd like to get" a sum of frequencies? That's my guess, but the word "sum" (and its synonyms) have not appeared in this discussion so far. So let's take this a step further Is the desired output any one of the following? frequency that simultaneously A='C' and B='C' and C='C' and D='C' frequency A='C' + frequency B='C' + frequency C='C' + frqeuency D='C' something else

PaigeMiller · ‎02-08-2013

Not what I asked for. I don't want a word description. I don't think I can work from your word descriptions. I want to see an example of the resulting table that you wish to get.

PaigeMiller · ‎02-08-2013

I request at this time that you provide much more specificity about what the desired output is. As in, give an example of the output you want. Your words do not convey to me exactly what you are trying to achieve.

PaigeMiller · ‎02-08-2013

proc freq data=whatever; by facility_prov_code; tables a/out=var_a_freq; run; data var_a_freq; set var_a_freq; where a='c'; run;

PaigeMiller · ‎02-08-2013

It would help to see your code. This should be nothing more difficult than in a DATA step after PROC FREQ, selecting only the case where A='c' and B='c' and C='c' and D='c'.

PaigeMiller · ‎02-08-2013

Have PROC FREQ create an output data set, then select only the values where the variable has value c

PaigeMiller · ‎02-08-2013

There's no way we can answer your question. We can't see your data, we can't see the calculations you did, and we can't see the SAS output.

PaigeMiller · ‎02-07-2013

The problem is that you have to analyze the design in an appropriate manner. So if Year is crossed with the other factors, then it can't be a split-split plot (I don't think). So, I think this is the analysis Year and Nematode and their interaction is the whole plot, treatment is plot, cereal cultivar nested within Year is subplot. So this is my analysis proc mixed; class year blk nem trt cultivar; model lgsprCysts100=nem|year trt trt*nem trt*year trt*nem*year cultivar(year) cultivar(year)*nem cultivar(year)*trt cultivar(year)*new*trt; random blk blk*trt blk*trt*nem blk*trt*year blk*trt*year*nem; run; Maybe I'm way off. What do you think, ?

PaigeMiller · ‎02-07-2013

If each year a different cereal was planted, then cereal is nested within year, but year could still be crossed with treatment and nematodes. Could you confirm? These are the types of details that we require if we are going to provide a meaningful and correct answer.

PaigeMiller · ‎02-07-2013

Is Year crossed with the other factors?

PaigeMiller · ‎02-07-2013

There are examples of using PROC MIXED for split plots in both the online SAS help, plus in the book "SAS System for Mixed Models" by Littell, Milliken Stroup and Wolfinger. You really haven't given us enough details about the design for us to give more explicit instructions.

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